Simple way to spot landing pages and keywords to fix

Knowing where to start making improvements to your site can be difficult. But here’s a very simple technique which will make the trouble-spots stand out. You can use this trick in loads of Google Analytics reports. It will work for keywords, for landing pages, for referring sites. The technique is quick, easy and universal.

The golden metric is the bounce rate, of course. But the trick is to filter the data appearing in each report to focus on the trouble spots and the hot spots. Then draw your lessons and make your changes.

The secret is the “advanced filter” link which appears next to the inline filter at the bottom of your reports.

This simple text link opens up a powerful (almost hidden) form for filtering reports in multiple ways. It’s the kind of tiny feature which can be every bit as useful in real life as some of the big new ‘bells and whistles’ which Google make more fuss about.

You can probably figure out uses for this without me having to explain. This tip makes it much easier to narrow down a list of pages (or keywords, or whatever) in order to spot the problem areas and take action. Once you start using this you’ll find it will come in handy again and again.

Please give it a try, it’s really simple and it often makes the insight almost jump out at you. The more I use this feature, the more I like it.

If you’d like an example, please read on.

The advanced inline filters interface is hidden behind a text link, so you might not notice it at first. But you may already be using the original inline filter to narrow down keywords, for example, to ones which contain or exclude brand terms. That technique is also good for grouping types of page, such as all the product pages or all the category pages on an ecommerce site. I would typically use that approach when looking at the product pages vs the category pages as landing pages.

In the case of a set of category landing pages you might then click on the bounce rate column in order to sort them in descending order to spot the worst examples. But the snag with this is that the worst cases may (we hope!) turn out to be odd pages which have attracted a tiny level of traffic which happened to bounce. They’re not a priority.

This is where the advanced inline filter comes in handy. You need to set a bottom threshold to the report. Just add an extra condition to your filter so that you only see the landing pages where a significant number of people entered the site:

Suddenly the report comes into focus and you can see the pages which matter: just the ones with significant traffic, with the ones with the highest bounce rate at the top. Details of the advanced filter can be seen at the top of the report, while the ‘edit’ and ‘clear’ filter links are at the bottom.

[Update: but please check the links in the resources section at the bottom of this post to see why Google Analytics ‘Weighted Sort’ fetaure, introduced after this post was written, provides a new and better way of doing this for most cases.]

When you’re doing this kind of analysis, it’s also good to switch to the comparison view of the data.

The comparison view makes it easy to judge the relative scale of the issues. This is one of the most useful views of data in Google Analytics. As with advanced inline filters, the interface does not draw much attention to a very powerful and simple tool. This is one of my favourite ways of letting GA help me spot where the problems lie. Just give that little icon up at the top right of the table a click and see for yourself!

In the example above you can see that most of the bounce rate bars are green and to the right = good. This shows that most of the category pages have a lower bounce rate than the site average.

In this case the data is from a blog, not an ecommerce site, but on a shopping site it is often the case that the category pages and the home page will both have lower than average bounce rates. Both are ‘routing’ pages: their function is to present options for further navigation and to route people to other pages. So ideally both should be better than average in earning at least one exploratory click.

But two of those bars are red and to the left. So why are people so much more likely to bounce from those category pages? The next step is to look at the pages themselves and the sources of the visitors landing on them to see if there’s an explanation.

In this example the really bad category page on this blog about skateboard history is a category called ‘skateboard competitions’. And the visitors are coming from search. So it’s not difficult to guess what is going on. These are almost certainly young skateboarders hoping to enter a skateboard competition of some kind. When they land on a page containing information about events which took place before they were born, they back out of there fast.

You might find something similar on an ecommerce site: visitors landing on a category page after searching for something which seems to match in terms of pure search phrases, but is actually completely irrelevant. For example searches for red ballet shoes landing on your red shoes category page. But you sell trainers. If those are paid search clicks, go running to your negative keywords list. If it’s organic search, take a close look at your Title field and Meta Description field: can you make it more obvious what kind of shoes you sell?

I’ve shown a landing page report in this example, but this approach works very well with keywords. In fact these functions are available throughout Google Analytics. The principle is the same:

Narrow the rows of data down so that you’re only looking at the traffic which matters and not drowning in numbers

Sort, or use the comparison view, so that you see what’s bad and what’s good

Examine the pages and the traffic source data to see if you can work out what’s happening

Then make changes

That last bit’s harder than clicking buttons in an analytics tool of course. But that’s where the fun and the satisfaction really is.

Acknowledgments: of course Avinash Kaushik was a strong influence on this! In this particular case I think it was a presentation at SES London. But if not there, somewhere else. And he’s certainly blogged about other great examples of using Advanced Filters.